Using purrr for quick summary tables - St Koo
Fitting several linear models by group with purrr - Anna Quaglieri
Column-wise operations with mutate, across and case_when - Anna Quaglieri
Basic but Useful R Tips by Song
Render an RMarkdown report - Sehrish Kanwal
- Advantages:
Pathwork - Shazia Ruybal
How to Generate Word Clouds in R (The simplest way) - Zeinab Manzari
Insert a time-stamp in RStudio - Adele Barughase

Using `purrr` for quick summary tables - St Koo

Adapted from this fantasic Learn to Purrr tutorial

# Load example data
data("mtcars")

head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

You can use purrr to loop over all the columns, and output the info into a dataframe.

In this example, I want to see variable type, max character length, and missingness.

library(tidyverse) # Includes purrr

# Create dataframe with some summary info
summary_df <- mtcars %>%
  purrr::map_df(
    ~data.frame(
      class = class(.x),
      max_char = max(nchar(.x, keepNA = FALSE)),
      missing = sum(is.na(.x))
    ), .id ="col_name"
  )

summary_df

##    col_name   class max_char missing
## 1       mpg numeric        4       0
## 2       cyl numeric        1       0
## 3      disp numeric        5       0
## 4        hp numeric        3       0
## 5      drat numeric        4       0
## 6        wt numeric        5       0
## 7      qsec numeric        5       0
## 8        vs numeric        1       0
## 9        am numeric        1       0
## 10     gear numeric        1       0
## 11     carb numeric        1       0

And because it’s a dataframe, you can use a package like kableExtra to format it for reports.

library(kableExtra)

summary_df %>%
  kableExtra::kbl() %>%
  kableExtra::kable_styling()

col_name	class	max_char
mpg	numeric	4
cyl	numeric	1
disp	numeric	5
hp	numeric	3
drat	numeric	4
wt	numeric	5
qsec	numeric	5
vs	numeric	1
am	numeric	1
gear	numeric	1
carb	numeric	1

Fitting several linear models by group with `purrr` - Anna Quaglieri

For the example I am going to use the flights dataset from the R package nycflights13. I am going to fit linear model that tries to explain the arr_time as a function of dep_time and arr_delay.

library(nycflights13)
library(purrr)

flights %>%
  dplyr::select(arr_time, dep_time, arr_delay, carrier) %>%
  head()

## # A tibble: 6 x 4
##   arr_time dep_time arr_delay carrier
##      <int>    <int>     <dbl> <chr>  
## 1      830      517        11 UA     
## 2      850      533        20 UA     
## 3      923      542        33 AA     
## 4     1004      544       -18 B6     
## 5      812      554       -25 DL     
## 6      740      554        12 UA

To fit the model to the whole dataset we would use the following code:

summary(lm(arr_time ~ dep_time + arr_delay, data = flights))

## 
## Call:
## lm(formula = arr_time ~ dep_time + arr_delay, data = flights)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2250.59   -68.14    50.97   169.26  2342.72 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 491.250510   2.045943   240.1   <2e-16 ***
## dep_time      0.757657   0.001446   524.1   <2e-16 ***
## arr_delay    -1.633355   0.015815  -103.3   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 392.8 on 327343 degrees of freedom
##   (9430 observations deleted due to missingness)
## Multiple R-squared:  0.4566, Adjusted R-squared:  0.4566 
## F-statistic: 1.375e+05 on 2 and 327343 DF,  p-value: < 2.2e-16

What if we wanted to fit separate models by carrier?

models <- flights %>%
  dplyr::select(arr_time, dep_time, arr_delay, carrier) %>%
  tidyr::nest(-carrier) %>%
  dplyr::mutate(fit = purrr::map(data, ~ lm(arr_time ~ dep_time + arr_delay, data = .))) %>%
  dplyr::mutate(results_fit = purrr::map(fit, function(f) confint(f))) 
models

## # A tibble: 16 x 4
##    carrier data                  fit    results_fit      
##    <chr>   <list>                <list> <list>           
##  1 UA      <tibble [58,665 × 3]> <lm>   <dbl[,2] [3 × 2]>
##  2 AA      <tibble [32,729 × 3]> <lm>   <dbl[,2] [3 × 2]>
##  3 B6      <tibble [54,635 × 3]> <lm>   <dbl[,2] [3 × 2]>
##  4 DL      <tibble [48,110 × 3]> <lm>   <dbl[,2] [3 × 2]>
##  5 EV      <tibble [54,173 × 3]> <lm>   <dbl[,2] [3 × 2]>
##  6 MQ      <tibble [26,397 × 3]> <lm>   <dbl[,2] [3 × 2]>
##  7 US      <tibble [20,536 × 3]> <lm>   <dbl[,2] [3 × 2]>
##  8 WN      <tibble [12,275 × 3]> <lm>   <dbl[,2] [3 × 2]>
##  9 VX      <tibble [5,162 × 3]>  <lm>   <dbl[,2] [3 × 2]>
## 10 FL      <tibble [3,260 × 3]>  <lm>   <dbl[,2] [3 × 2]>
## 11 AS      <tibble [714 × 3]>    <lm>   <dbl[,2] [3 × 2]>
## 12 9E      <tibble [18,460 × 3]> <lm>   <dbl[,2] [3 × 2]>
## 13 F9      <tibble [685 × 3]>    <lm>   <dbl[,2] [3 × 2]>
## 14 HA      <tibble [342 × 3]>    <lm>   <dbl[,2] [3 × 2]>
## 15 YV      <tibble [601 × 3]>    <lm>   <dbl[,2] [3 × 2]>
## 16 OO      <tibble [32 × 3]>     <lm>   <dbl[,2] [3 × 2]>

expand_models <- models %>%
  tidyr::unnest(results_fit, .drop=TRUE) 
expand_models

## # A tibble: 48 x 4
##    carrier data                  fit    results_fit[,1]    [,2]
##    <chr>   <list>                <list>           <dbl>   <dbl>
##  1 UA      <tibble [58,665 × 3]> <lm>           491.    511.   
##  2 UA      <tibble [58,665 × 3]> <lm>             0.757   0.771
##  3 UA      <tibble [58,665 × 3]> <lm>            -1.83   -1.66 
##  4 AA      <tibble [32,729 × 3]> <lm>           434.    455.   
##  5 AA      <tibble [32,729 × 3]> <lm>             0.822   0.838
##  6 AA      <tibble [32,729 × 3]> <lm>            -1.02   -0.852
##  7 B6      <tibble [54,635 × 3]> <lm>           843.    870.   
##  8 B6      <tibble [54,635 × 3]> <lm>             0.406   0.425
##  9 B6      <tibble [54,635 × 3]> <lm>            -2.66   -2.42 
## 10 DL      <tibble [48,110 × 3]> <lm>           418.    436.   
## # … with 38 more rows

expand_models$fit[1]

## [[1]]
## 
## Call:
## lm(formula = arr_time ~ dep_time + arr_delay, data = .)
## 
## Coefficients:
## (Intercept)     dep_time    arr_delay  
##     501.083        0.764       -1.746

Column-wise operations with `mutate`, `across` and `case_when` - Anna Quaglieri

I found the method below really useful to recode the levels of one or several columns!

library(dplyr)

test_data <- data.frame(area = rep(c("North", "Sud", "East", "West"),times = c(2,3,4,1)),
                        quality_before = c("High","Low",
                                    "High","Low","Medium",
                                    "Medium","Low","High","High",
                                    "Low"),
                        quality_after = c("High","High",
                                    "High","Medium","Medium",
                                    "Low","Low","High","High",
                                    "Low"))

test_data %>%
  mutate(across(.cols = c(quality_before, quality_after),
            ~ case_when(
              . == "Low" ~ 0,
              . == "Medium" ~ 1,
              . == "High" ~ 2
            )
          )
  )

##     area quality_before quality_after
## 1  North              2             2
## 2  North              0             2
## 3    Sud              2             2
## 4    Sud              0             1
## 5    Sud              1             1
## 6   East              1             0
## 7   East              0             0
## 8   East              2             2
## 9   East              2             2
## 10  West              0             0

Strongly suggest to have a look at other functions and applications to perform column-wise operations https://cran.r-project.org/web/packages/dplyr/vignettes/colwise.html.

Basic but Useful R Tips by Song

Before we start

sessionInfo()

## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.6
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] nycflights13_1.0.1 kableExtra_1.3.1   forcats_0.5.0      stringr_1.4.0     
##  [5] dplyr_1.0.0        purrr_0.3.4        readr_1.3.1        tidyr_1.1.0       
##  [9] tibble_3.0.4       ggplot2_3.3.2      tidyverse_1.3.0    knitr_1.30        
## [13] png_0.1-7          magick_2.4.0      
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.1.0  xfun_0.19         haven_2.3.1       lattice_0.20-41  
##  [5] colorspace_2.0-0  vctrs_0.3.4       generics_0.0.2    viridisLite_0.3.0
##  [9] htmltools_0.5.0   yaml_2.2.1        utf8_1.1.4        blob_1.2.1       
## [13] rlang_0.4.8       pillar_1.4.6      withr_2.3.0       glue_1.4.2       
## [17] DBI_1.1.0         dbplyr_1.4.4      modelr_0.1.8      readxl_1.3.1     
## [21] lifecycle_0.2.0   munsell_0.5.0     gtable_0.3.0      cellranger_1.1.0 
## [25] rvest_0.3.5       evaluate_0.14     fansi_0.4.1       highr_0.8        
## [29] broom_0.5.6       Rcpp_1.0.5        backports_1.2.0   scales_1.1.1     
## [33] webshot_0.5.2     jsonlite_1.7.1    fs_1.4.2          hms_0.5.3        
## [37] digest_0.6.27     stringi_1.5.3     cli_2.1.0         tools_4.0.2      
## [41] magrittr_1.5      crayon_1.3.4      pkgconfig_2.0.3   ellipsis_0.3.1   
## [45] xml2_1.3.2        reprex_0.3.0      lubridate_1.7.9   assertthat_0.2.1 
## [49] rmarkdown_2.3     httr_1.4.2        rstudioapi_0.13   R6_2.5.0         
## [53] nlme_3.1-148      compiler_4.0.2

Data Exploration

I code in tidyverse universe plus tidylog to output all message corresponding to changes made to vector, dataframe, tibble, etc. Please find tidylog package @ https://github.com/elbersb/tidylog.

An initial step is often to preview a dataset. An alternative to summary is describe in Hmisc package. The benefits will be it counts number of NAs as summary does, but it also show a frequency table for factor/character.

library(tidyverse)

# colnames(iris)
# summary(iris)
# str(iris)

library(Hmisc)

iris$Species %>% describe

## . 
##        n  missing distinct 
##      150        0        3 
##                                            
## Value          setosa versicolor  virginica
## Frequency          50         50         50
## Proportion      0.333      0.333      0.333

tidylog reduces significant code verification and avoid many errors for me. It warns you when NA is generated due to situation not considered.

library(tidylog, warn.conflicts = FALSE, quietly = FALSE)

new_dt <- iris %>% 
  filter(Sepal.Length >= 4.6) %>% 
  mutate(new_name = case_when(
    Species == "versicolor" ~ "V",
    Species == "setosa" ~ "S"))

Compare with revised code: no new NA was generated after the mutation.

library(tidylog, warn.conflicts = FALSE, quietly = FALSE)

new_dt <- iris %>% 
  filter(Sepal.Length >= 4.6) %>% 
  mutate(new_name = case_when(
    Species == "versicolor" ~ "Versicolor",
    Species == "setosa" ~ "Setosa",
    TRUE ~ "Virginica"))

Data Preparation

Use iris dataframe as an example.

relocate also support .after, and combine use with where.

dt <- head(iris,5)

# dt %>% select("Species", everything(.))
dt %>% relocate("Species", .before = "Sepal.Length")

##   Species Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1  setosa          5.1         3.5          1.4         0.2
## 2  setosa          4.9         3.0          1.4         0.2
## 3  setosa          4.7         3.2          1.3         0.2
## 4  setosa          4.6         3.1          1.5         0.2
## 5  setosa          5.0         3.6          1.4         0.2

# dt %>% relocate(where(is.numeric), .after = where(is.factor))

Similar concept can be applied to a vector through SOfun package. I found this useful when adjusting factor levels. Of course, fct_reorder and fct_relevel are useful in different situations.

library(devtools)
# install_github("mrdwab/SOfun", force=TRUE)
library(SOfun)
v <- letters[1:7]
v %>% moveMe(., "a last; b, e, g before d; c first; g after b")

## [1] "c" "b" "g" "e" "d" "f" "a"

Another high frequency task is to manage NAs. This is way to spot hidden NAs.

hidden_na_dt <- data.frame(
  "student" = rep(c("A", "B", "C"),2),
  "assignment" = rep(c("A1", "A2"),3),
  "mark" = c(NA, runif(n = 5, min = 45, max = 100))
) %>% 
  filter(!is.na(mark))

hidden_na_dt

##   student assignment     mark
## 1       B         A2 81.66840
## 2       C         A1 83.72778
## 3       A         A2 75.92982
## 4       B         A1 87.81650
## 5       C         A2 87.53854

apply complete from dplyr package to fill 0 in missing mark from assignment 1 for student A. If there is more combinations, multiple items can be nesting in the complete argument.

hidden_na_dt  %>% 
  complete(student, nesting(assignment), fill = list(mark = 0))

## # A tibble: 6 x 3
##   student assignment  mark
##   <chr>   <chr>      <dbl>
## 1 A       A1           0  
## 2 A       A2          75.9
## 3 B       A1          87.8
## 4 B       A2          81.7
## 5 C       A1          83.7
## 6 C       A2          87.5

Data Visualisation

I believe ggplot2 / plotly is relative popular in practice. I also recommend highercharter to visualize timeseries data and/or visNetwork / igraph / ggraph to visualize networks. My focus today is labeling inside a chart, so that I will use ggplot2 to demonstrate.

Randomly picked a few countries by max number of population of that country to show potential difference when treating labeling.

plt_original <- population %>% 
  filter(country %in% c("India", "United States of America", "Viet Nam",
                        "Lao People's Democratic Republic")) %>% 
  ggplot(aes(x = year, y = population, group = country, color = country))+
  geom_line()

plt_original

Functions that I used to improve numeric formatting

The purpose of having customized functions is to improve readability and reduce cognitive load for digesting information provided by visualization.

The function I grabbed from stackoverflow and made two adaptations: (1) allow the function to accept negative inputs and (2) expand to recognize trillions.

si_num <- function (x) {
  
  if (!is.na(x)) {
    
    if (x < 0){ 
      sign <-  "-"
      x <- abs(x)
    }else{
      sign <-  ""
      x <- x
    }
    
    if (x >= 1e12) { 
      chrs <- strsplit(format(x, scientific=12), split="")[[1]];
      len <- chrs[seq(1,length(chrs)-12)] %>% length();
      rem <- chrs[seq(1,length(chrs)-11)];
      rem <- append(rem, ".", after = len) %>% append("T");
    }
        
    if (x >= 1e9) { 
      chrs <- strsplit(format(x, scientific=12), split="")[[1]];
      len <- chrs[seq(1,length(chrs)-9)] %>% length();
      rem <- chrs[seq(1,length(chrs)-8)];
      rem <- append(rem, ".", after = len) %>% append("B");
    }
    
    
    else if (x >= 1e6) { 
      chrs <- strsplit(format(x, scientific=12), split="")[[1]];
      len <- chrs[seq(1,length(chrs)-6)] %>% length();
      rem <- chrs[seq(1,length(chrs)-5)]
      rem <- append(rem, ".", after = len) %>% append("M");
    }
    
    else if (x >= 1e3) { 
      chrs <- strsplit(format(x, scientific=12), split="")[[1]];
      len <- chrs[seq(1,length(chrs)-3)] %>% length();
      rem <- chrs[seq(1,length(chrs)-2)];
      rem <- append(rem, ".", after = len) %>% append("K");
    }
    
    else {
      return(x);
    }
    
    return(str_c(sign, paste(rem, sep="", collapse=""), sep = ""));
  }
  else return(NA);
} 

si_vec <- function(x) {
  sapply(x, FUN=si_num);
}

Modifications include: (1) change graph title and axis titles and format, (2) change a theme: minimalist design, (3) remove legend and add text labels to each line.
Of course, there are more things: change color pallet defined for country, graph size,…

library(hrbrthemes)
library(scales)
library(ggrepel)
library(cowplot)


year_series <- unique(population$year)
reminder <- (max(year_series) - min(year_series)) %% 4
new_breaks <- seq(from = min(year_series) + reminder, to = max(year_series), by = 4) 

df <- population %>% 
  filter(country %in% c("India", "United States of America", "Viet Nam",
                        "Lao People's Democratic Republic")) 
df_end <- df %>% 
  group_by(country) %>% 
  filter(year == max(year)) %>% 
  ungroup()

plt_adjust <- df %>% 
  ggplot(aes(x = year, y = population, group = country, color = country))+
  geom_line()+
  geom_point()+
  geom_text_repel(
    data = df_end,
    aes(label = str_wrap(country,25)),
    nudge_x = 1,
    direction = "y",## nudge vertically
    size = 3,
    hjust = 0, ### left aligned
    segment.size = 0.3, ### from here is about the line to connect the data point and text
    min.segment.length = 0,
    segment.color = "grey60") + 
  theme_ipsum() +
  theme(legend.position = "none") +
  scale_y_continuous(labels = si_vec)+
  scale_x_continuous(breaks = new_breaks, limits = c(NA, 2020))+
  labs(x = "Year", y = "Population", title = "Population Growth between 1995 and 2013")


plt_original

plt_adjust

Or, put it into plotly, the default hover over message often does not satisfy users, more professional format is recommended to be used in hover over text.

library(plotly)

plt_plotly <- df %>% 
  mutate(text = str_c("Country: ", country, "\n",
                      "Year: ", year, "\n",
                      "Population: ", si_vec(population))) %>% 
  ggplot(aes(x = year, y = population, group = country, color = country, text = text))+
  geom_line()+
  geom_point()+
  theme_ipsum() +
  theme(legend.position = "none") +
  scale_y_continuous(labels = si_vec)+
  scale_x_continuous(breaks = new_breaks)+
  labs(x = "Year", y = "Population", title = "Population Growth between 1995 and 2013")



ggplotly({plt_plotly}, tooltip = "text")

At the end

To be continue, I have coded many interactive plots in shinyapps, and some can be found from https://coffeeandplot.com/apps/. This is a relatively new website we created couples of month ago. Get in touch if you have any suggestions. Please find me @ https://www.linkedin.com/in/ytsong/.

Render an RMarkdown report - Sehrish Kanwal

This section describes how to render an RMarkdown report within a simple R conda environment on a Command Line Interface (cluster or linux environment). This could be achieved in two possible ways:

Creating/activating a conda environment and installing packages on commandline
Using an environment.yml file that documents the package dependencies

Both work but the second way is the recommended one, which will be described below.

Create an environment.yml file, that looks something like

#name of the conda environment
name: HowRYou

#the paths that conda takes a look for packages. Avoid using anaconda channel as we have
#experienced issues using it 
channels:
    - conda-forge
    - bioconda
    - defaults

#install following packages in the conda environment
#change according to the packages you are using in your RMardown file. 
#The first three are required (are R essentail). You can also change the versions to
# meet the requirements of your analysis 
dependencies:
    - r-base=3.4.1
    - pandoc=1.19
    - r-rmarkdown=1.6
    - r-here

Create a conda environment (in this case HowRYou is the conda environment name specified in the environment.yml file. -p flag should point to your miniconda installation path. To find how to install conda, check this
```
conda env create -p /path/to/miniconda/envs/HowRYou --file environment.yml
```
Activate this conda environment
```
conda activate HowRYou
```

Run the RMarkdown file

Rscript -e "rmarkdown::render('HowRYou.Rmd')"

To pass arguments to the Rmd script (in this case two arguments - an input directory location and name of the input vcf file)

Rscript -e "rmarkdown::render('HowRYou.Rmd', params = list(directory = './data', file = 'dummy.txt'))"

An example of a rendered script used in the above step # 4

Advantages:

Reproducibility - ability to perform the analysis multiple times
Portability - being able to move code from one machine or platform to another
Flexibility - change easily in response to different user and system requirements

Pathwork - Shazia Ruybal

# install.packages(c("tidyverse", "patchwork, palmerpenguins", "devtools"))
# devtools::install_github("bahlolab/ggwehi")
library(tidyverse)
library(patchwork)
# library(ggwehi)
library(palmerpenguins)

plot_1 <- penguins %>%
  ggplot(aes(x = flipper_length_mm, y = body_mass_g, color = species)) +
    geom_point() +
   # scale_color_wehi() +
    labs(x = "Flipper length (mm)",
         y = "Body mass (g)",
         color = "Penguin species") +
    theme_minimal()

plot_2 <- penguins %>% 
  ggplot(aes(x = flipper_length_mm, fill = species)) +
    geom_histogram(alpha = 0.5, position = "identity") +
   # scale_fill_wehi() +
    labs(x = "Flipper length (mm)",
         y = "Frequency",
         fill = "Penguin species") +
    theme_minimal()

plot_1 + plot_2 + # plot them side-by-side
  plot_layout(guides = "collect") + # this "collects" the legends to the right of the plot
  plot_annotation(tag_levels = "a", tag_suffix = ")") # this adds panel labels

plot_1 / plot_2 + # plot them one over the other
  plot_layout(guides = "collect") + # this "collects" the legends to the right of the plot
  plot_annotation(tag_levels = "a", tag_suffix = ")") # this adds panel labels

How to Generate Word Clouds in R (The simplest way) - Zeinab Manzari

# install.packages(c("wordcloud","RColorBrewer))
library(wordcloud) #wordcloud for generating word cloud images
library(RColorBrewer) #RColorBrewer for color palettes

words <- c("RLadies", "Rmarkdown", "tips", "tricks", "script", "rmd", "console", "packages", "share", "4thanniversary", "celebrate", 
           "RSoftware", "Australia", "Melbourne", "Girls", "Learn","Teach", "Data Structure", "Algorithm", "Visualisation", 
           "Code", "Data", "ggplot2", "Zoom", "Help", "Text", "RStudio", "programing", "questions", "answers", "Plot", "happy")

freqs <- c(980, 900, 498, 811, 800, 654, 489, 90, 254, 500, 600, 200, 488, 400, 140, 250, 357, 789, 147, 120, 590, 741, 100, 788, 812, 693, 410, 753, 95, 80, 594, 644)

set.seed(3)

wordcloud(words = words,freqs = freqs, scale=c(4,.4), max.words = 1000, random.order=TRUE, random.color=TRUE, colors = brewer.pal(8, "Accent"))

Insert a time-stamp in RStudio - Adele Barughase

In a code chunk or in an R script, insert a timestamp with ts + shift + tab.

First write ts:

ts

## function (data = NA, start = 1, end = numeric(), frequency = 1, 
##     deltat = 1, ts.eps = getOption("ts.eps"), class = if (nseries > 
##         1) c("mts", "ts", "matrix") else "ts", names = if (!is.null(dimnames(data))) colnames(data) else paste("Series", 
##         seq(nseries))) 
## {
##     if (is.data.frame(data)) 
##         data <- data.matrix(data)
##     if (is.matrix(data)) {
##         nseries <- ncol(data)
##         ndata <- nrow(data)
##         dimnames(data) <- list(NULL, names)
##     }
##     else {
##         nseries <- 1
##         ndata <- length(data)
##     }
##     if (ndata == 0) 
##         stop("'ts' object must have one or more observations")
##     if (missing(frequency)) 
##         frequency <- 1/deltat
##     else if (missing(deltat)) 
##         deltat <- 1/frequency
##     if (frequency > 1 && 0 < (d <- abs(frequency - round(frequency))) && 
##         d < ts.eps) 
##         frequency <- round(frequency)
##     if (!missing(start)) 
##         start <- as.numeric(start)
##     if (length(start) > 1L) {
##         start <- start[1L] + (start[2L] - 1)/frequency
##     }
##     if (!missing(end)) 
##         end <- as.numeric(end)
##     if (length(end) > 1L) {
##         end <- end[1L] + (end[2L] - 1)/frequency
##     }
##     if (missing(end)) 
##         end <- start + (ndata - 1)/frequency
##     else if (missing(start)) 
##         start <- end - (ndata - 1)/frequency
##     if (start > end) 
##         stop("'start' cannot be after 'end'")
##     cycles <- as.numeric((end - start) * frequency)
##     if (abs(round(cycles) - cycles) > ts.eps * max(cycles, 1)) 
##         stop("'end' must be a whole number of cycles after 'start'")
##     nobs <- floor(cycles + 1.01)
##     if (nobs != ndata) 
##         data <- if (NCOL(data) == 1) {
##             if (ndata < nobs) 
##                 rep_len(data, nobs)
##             else if (ndata > nobs) 
##                 data[1L:nobs]
##         }
##         else {
##             if (ndata < nobs) 
##                 data[rep_len(1L:ndata, nobs), ]
##             else if (ndata > nobs) 
##                 data[1L:nobs, ]
##         }
##     attr(data, "tsp") <- c(start, end, frequency)
##     if (!is.null(class) && class[[1]] != "none") 
##         attr(data, "class") <- class
##     data
## }
## <bytecode: 0x7fa31cb92830>
## <environment: namespace:stats>

Then press shift + tab:

# Wed Nov 11 10:42:54 2020 ------------------------------

A work by R-Ladies Melbourne

How R you? - R-Ladies Melbourne 4th anniversary!

16/11/2020

Using `purrr` for quick summary tables - St Koo

Fitting several linear models by group with `purrr` - Anna Quaglieri

Column-wise operations with `mutate`, `across` and `case_when` - Anna Quaglieri

Basic but Useful R Tips by Song

Before we start

Data Exploration

Data Preparation

Data Visualisation

Functions that I used to improve numeric formatting

At the end

Render an RMarkdown report - Sehrish Kanwal

Advantages:

Pathwork - Shazia Ruybal

How to Generate Word Clouds in R (The simplest way) - Zeinab Manzari

Insert a time-stamp in RStudio - Adele Barughase

How R you? - R-Ladies Melbourne 4th anniversary!

16/11/2020

Using purrr for quick summary tables - St Koo

Fitting several linear models by group with purrr - Anna Quaglieri

Column-wise operations with mutate, across and case_when - Anna Quaglieri

Basic but Useful R Tips by Song

Before we start

Data Exploration

Data Preparation

Data Visualisation

Functions that I used to improve numeric formatting

At the end

Render an RMarkdown report - Sehrish Kanwal

Advantages:

Pathwork - Shazia Ruybal

How to Generate Word Clouds in R (The simplest way) - Zeinab Manzari

Insert a time-stamp in RStudio - Adele Barughase

Using `purrr` for quick summary tables - St Koo

Fitting several linear models by group with `purrr` - Anna Quaglieri

Column-wise operations with `mutate`, `across` and `case_when` - Anna Quaglieri